Differentially Private Obfuscation of Power Grid Dynamics

Abstract

Dynamic models of power systems are critical for analyzing grid response to disturbances and blackouts, but the release of real-world dynamic models is hindered by privacy and cybersecurity concerns, as such models carry sensitive information about transmission, generation, and load parameters. We develop an algorithm for synthesizing dynamic grid models from real-world power grids balancing two objectives: the privacy of the source grid, quantitatively measured using the notion of differential privacy, and the fidelity of the synthesized model. The algorithm applies privacy-preserving noise to obfuscate the original grid parameters, but then optimizes the perturbed parameters to ensure that the resulting model dynamics are statistically consistent with those observed in the source grid. Application to the frequency dynamics of the IEEE 30-bus system reveals the inherent privacy-fidelity trade-off: stricter privacy requirements degrade modeling fidelity, yet optimization significantly improves the quality of the synthesized models.